How Clustering Affects the Convergence of Decentralized Optimization over Networks: A Monte-Carlo-based Approach Article Swipe
Mohammadreza Doostmohammadian
,
Shahaboddin Kharazmi
,
Hamid R. Rabiee
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.01460
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.01460
Decentralized algorithms have gained substantial interest owing to advancements in cloud computing, Internet of Things (IoT), intelligent transportation networks, and parallel processing over sensor networks. The convergence of such algorithms is directly related to specific properties of the underlying network topology. Specifically, the clustering coefficient is known to affect, for example, the controllability/observability and the epidemic growth over networks. In this work, we study the effects of the clustering coefficient on the convergence rate of networked optimization approaches. In this regard, we model the structure of large-scale distributed systems by random scale-free (SF) and clustered scale-free (CSF) networks and compare the convergence rate by tuning the network clustering coefficient. This is done by keeping other relevant network properties (such as power-law degree distribution, number of links, and average degree) unchanged. Monte-Carlo-based simulations are used to compare the convergence rate over many trials of SF graph topologies. Furthermore, to study the convergence rate over real case studies, we compare the clustering coefficient of some real-world networks with the eigenspectrum of the underlying network (as a measure of convergence rate). The results interestingly show higher convergence rate over low-clustered networks. This is significant as one can improve the learning rate of many existing decentralized machine-learning scenarios by tuning the network clustering.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.01460
- https://arxiv.org/pdf/2407.01460
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400337821
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400337821Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.01460Digital Object Identifier
- Title
-
How Clustering Affects the Convergence of Decentralized Optimization over Networks: A Monte-Carlo-based ApproachWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-01Full publication date if available
- Authors
-
Mohammadreza Doostmohammadian, Shahaboddin Kharazmi, Hamid R. RabieeList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.01460Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2407.01460Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2407.01460Direct OA link when available
- Concepts
-
Monte Carlo method, Convergence (economics), Cluster analysis, Computer science, Mathematical optimization, Mathematics, Artificial intelligence, Economics, Statistics, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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